def test(X, y, learned_params):
N = np.shape(X)[0] #no of instances
X = np.append(np.ones((N,1)), X,1) #appending a column of ones as bias (used in logistic regression weights prediction)
F = np.shape(X)[1] #no of features+1
class_prob = []
for w in learned_params.keys():
prob = Utils.logistic_transformation(learned_params[w], X)
class_prob.append(prob)
max_prob = np.max(class_prob, 0)
predicted_y = []
output_label = range(min_class_label, max_class_label+1)
for i in xrange(np.size(max_prob)):
class_label = np.where(class_prob == max_prob[i])[0]
predicted_y.append(output_label[class_label[0]])
print "predicted y :", predicted_y
print "Actual y:", y
accuracy = Utils.calculate_accuracy(np.array(y), np.array(predicted_y))
print "accuracy for test data :", accuracy
f_score_mean, f_score_std = Utils.calculate_average_F1score(np.array(y), np.array(predicted_y), min_class_label, max_class_label)
print "Average f score for test data :", f_score_mean
error_rate = Utils.calculate_error_rate(np.array(y), np.array(predicted_y))
#ch = stdin.read(1)
return (accuracy, f_score_mean, f_score_std, error_rate)
